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Currently, Jiali Chen is the first-year direct Ph.D. student at Key Laboratory of Big Data and Intelligent Robot of South China University of Technology (SCUT), supervised by Prof. Yi Cai. He works closely with Dr. Jiayuan Xie at Hong Kong Polytechnic University (PolyU). Before that, He also obtained the B.E. degree in Department of Software Engineering from South China University of Technology (SCUT) in 2023. His research interests revolve around Multimodal Learning, Causal Inference and Text Generation. Feel free to contact me if you are interested in my work or seeking potential collaborations. |
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Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor
Jiali Chen, Xusen Hei, Yuqi Xue, Yuancheng Wei, Jiayuan Xie, Yi Cai, Qing Li ACM Multimedia, ACM MM 2024 [Paperlink], [Code] Area: Large Multimodal Model, New Benchmark We present the first work to investigate the error correction capabilities of large multimodal models (LMMs), construct a new benchmark and introduce the feedback generation task for evaluation. I would like to extend my heartfelt gratitude to my girlfriend, Ms. Wen, for inspiring the idea behind this paper. |
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Deconfounded Emotion Guidance Sticker Selection with Causal Inference
Jiali Chen, Yi Cai, Ruohang Xu, Jiexin Wang, Jiayuan Xie, Qing Li ACM Multimedia, ACM MM 2024 [Paperlink] Area: Bias, Causal Inference, Sticker Selection This paper presents a Causal Knowledge-Enhanced Sticker Selection model that addresses spurious correlations in sticker selection by using a causal graph and a knowledge-enhanced approach. |
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Knowledge-Guided Cross-Topic Visual Question Generation
Hongfei Liu, Guohua Wang, Jiayuan Xie, Jiali Chen, Wenhao Fang, Yi Cai International Conference on Computational Linguistics, COLING 2024 [Paperlink] Area: Cross-Topic, Text Generation We propose a knowledge-guided cross-topic visual question generation (KC-VQG) model to extract unseen topic-related information for question generation. |
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Video Question Generation for Dynamic Changes
Jiayuan Xie*, Jiali Chen*, Zhenghao Liu, Qingbao Huang, Yi Cai, Qing Li IEEE Transactions on Circuits and Systems for Video Technology, TCSVT 2024 [Paperlink], [Code] Area: Video Understanding, Text Generation We introduce D-VQG, a difference-aware video question generation model that aims to generate questions about temporal differences in the video. |
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Knowledge-Augmented Visual Question Answering with Natural Language Explanation
IEEE Transactions on Image Processing, TIP 2024 [Paperlink], [Code] Area: VQA, Reasoning, Text Generation We introduce KICNLE, which generates consistent answer and explanation with external knowledge. |
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Diverse Visual Question Generation based on Multiple Objects Selection
Wenhao Fang, Jiayuan Xie, Hongfei Liu, Jiali Chen, Yi Cai Transactions on Multimedia Computing Communications and Applications, TOMM 2024 [Paperlink] Area: Diverse Text Generation, Visual Question Generation We promote the semantic diversity of generated questions beyond the description diversity. |
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Deconfounded Visual Question Generation with Causal Inference
Jiali Chen, Zhenjun Guo, Jiayuan Xie, Yi Cai, Qing Li ACM Multimedia, ACM MM 2023 [Paperlink], [Code] Area: Bias, Causal Inference, Visual Question Generation This study first introduces a causal perspective on VQG and adopts the causal graph to analyze spurious correlations among variables. We propose KECVQG mitigates the impact of spurious correlations for VQG. |
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Visual Question Generation for Explicit Questioning Purposes based on Target Objects Jiayuan Xie*, Jiali Chen*, Wenhao Fang, Yi Cai, Qing Li Neural Network, NN 2023 [Paperlink], [Code] Area: Controllable Text Generation, Visual Question Generation We propose a content-controlled question generation model, which generates questions based on a given target object set specified from an image. |
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Visual Paraphrase Generation with Key Information Retained Jiayuan Xie, Jiali Chen, Yi Cai, Qingbao Huang, Qing Li ACM Transactions on Multimedia Computing, Communications and Applications, TOMM 2023 [Paperlink], [Code] Area: Vision-Language, Controllable Text Generation We propose an object-level paraphrase generation model, which generates paraphrases by adjusting the permutation of key objects and modifying their associated descriptions. |
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Category-Guided Visual Question Generation Hongfei Liu, Jiali Chen, Wenhao Fang, Jiayuan Xie, Yi Cai Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Student Abstract 2023 [Paperlink] Area: Controllable Text Generation, Visual Question Generation A category-guided visual question generation model that can generate questions with multiple categories that focus on different objects in an image. |
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Last updated on Jul, 2024
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